This function computes the intermediate correlation matrix by combining tetrachoric correlation for binary-binary combinations, biserial correlations for binary-normal combinations and Pearson correlation for normal-normal combinations. If the resulting correlation matrix is not positive definite, a nearest positive matrix will be used.

1 2 | ```
compute.sigma.star(no.bin, no.nor, prop.vec.bin = NULL,
corr.vec = NULL, corr.mat = NULL)
``` |

`no.bin` |
Number of binary variables |

`no.nor` |
Number of normal variables |

`prop.vec.bin` |
Probability vector for binary variables |

`corr.vec` |
Vector of elements below the diagonal of correlation matrix ordered columnwise |

`corr.mat` |
Specified correlation matrix |

`sigma_star` |
A resulting intermediate correlation matrix |

`nonPD` |
If a resulting intermediate correlation matrix is non-positive definite, it is stored in this value. Otherwise it is NULL. |

`PD` |
TRUE if |

`eigenv` |
Eigenvalues of the |

`validation.corr`

, `nearPD`

, `phi2poly`

, `is.positive.definite`

,

`jointly.generate.binary.normal`

, `simulation`

1 2 3 | ```
cmat = lower.tri.to.corr.mat(corr.vec= c(0.16, 0.04, 0.38, 0.14, 0.47, 0.68),4)
compute.sigma.star(no.bin=2, no.nor=2, prop.vec.bin=c(0.4,0.7),
corr.vec=NULL,corr.mat=cmat)
``` |

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